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Published at December 3

Reinforcement learning to learn quantum states for Heisenberg scaling accuracy

quant-ph

cs.AI

Released Date: December 3, 2024

Authors: Jeongwoo Jae¹, Jeonghoon Hong, Jinho Choo, Yeong-Dae Kwon

Aff.: ¹Samsung SDS, Seoul, Republic of Korea

Arxiv: http://arxiv.org/pdf/2412.02334v1

Refer to caption

No. qubits $N$	Quantum neural network	No. samples $k$	The space of sampling range ${\cal A}_{\sigma}^{4}$	The space of learning rate ${\cal A}_{\eta}^{4}$	Action repetition strategy $(t_{l},t_{u},T_{\text{th}})$
$1$	$U_{3}$	5	$\{1.0,0.1,0.01,0.001\}$	$\{1.0,0.1,0.01,0.001\}$	$(1,50,100)$
$2$	HEA, $L=1$	10	$\{1.0,0.1,0.01,0.001\}$	$\{1.0,0.1,0.01,0.001\}$	$(80,800,100)$
$3$	HEA, $L=5$	30	$\{0.1,0.01,0.001,0.0001\}$	$\{1.0,0.33,0.01,0.033\}$	$(300,2000,500)$ ¹¹1We obtain similiar results for $t_{l}=200$ .
$4$	HEA, $L=10$	100	$\{0.1,0.01,0.001,0.0001\}$	$\{1.0,0.33,0.01,0.033\}$	$t_{\text{rep}}=300$
$5$	HEA, $L=10$	100	$\{0.1,0.01,0.001,0.0001\}$	$\{1.0,0.33,0.01,0.033\}$	$t_{\text{rep}}=300$